CN111459992B - Information pushing method, electronic equipment and computer readable medium - Google Patents
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Abstract
The embodiment of the disclosure discloses an information pushing method, an electronic device and a computer readable medium. One embodiment of the method comprises: selecting target payment related information from the payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence; determining whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value; determining whether the rate of the target payment-related information of the target user satisfies a predetermined condition in response to determining that the conversion rate of the target payment-related information is greater than or equal to a preset threshold; and in response to determining that the target payment-related information rate meets a predetermined condition, transmitting the target payment-related information to the terminal device of the target user. According to the implementation method, the payment related information with the larger first target value is sent to the user, so that the click rate of the user on the payment related information is improved, and the network flow is increased.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an information pushing method, an electronic device, and a computer-readable medium.
Background
With the rapid development of computer technology, the number of applications for page interaction is increasing at present, and in order to attract more users, each platform sends various information to the users. The kinds of information and the amount of information are also rapidly increasing. How to improve the click rate of the user on the application by controlling the information sent to each user becomes a priority.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present disclosure propose an information push method, an electronic device, and a computer-readable medium to solve the technical problems mentioned in the above background section.
In a first aspect, some embodiments of the present disclosure provide an information pushing method, including: selecting target payment related information from the payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence, wherein the payment related information in the payment related information sequence has a corresponding conversion rate; determining whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value; determining whether the rate of the target payment-related information meets a predetermined condition in response to determining that the conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold; and sending the target payment related information to the terminal equipment of the target user in response to the fact that the rate of the target payment related information meets the preset condition.
In a second aspect, some embodiments of the present disclosure provide an information pushing apparatus, including: the system comprises a selection unit, a conversion unit and a processing unit, wherein the selection unit is configured to select target payment related information from a payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence, and the corresponding conversion rate exists in the payment related information sequence; a first determination unit configured to determine whether a conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold; a second determination unit configured to determine whether a rate of the target payment-related information satisfies a predetermined condition in response to a determination that a conversion rate of the target payment-related information of a target user is equal to or greater than a preset threshold value, wherein the rate is obtained based on the first target value and the second target value; a sending unit configured to send the target payment related information to the terminal device of the target user in response to determining that the rate of the target payment related information satisfies a predetermined condition.
In a third aspect, some embodiments of the present disclosure provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon which, when executed by one or more processors, cause the one or more processors to implement any of the methods described above.
In a fourth aspect, some embodiments of the disclosure provide a computer readable medium having a computer program stored thereon, where the program is to implement any of the above-mentioned methods when executed by a processor.
One of the above-described various embodiments of the present disclosure has the following advantageous effects: first, target payment related information is selected from the payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence. Then, whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value is determined. This ensures that the conversion rate of the target payment-related information is maintained within a certain range. Then, in response to determining that the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold, determining whether the rate of the target payment related information meets a preset condition; by determining the value range of the rate, the cost can be ensured not to be too high. And finally, in response to determining that the rate of the target payment related information meets a preset condition, sending the target payment related information to the terminal equipment of the target user. According to the embodiment, the payment related information with the larger first target value is sent to the user, so that the click rate of the user on the payment related information is improved, and the network flow is increased.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
FIG. 1 is a schematic diagram of one application scenario of an information push method according to some embodiments of the present disclosure;
FIG. 2 is a flow diagram of some embodiments of an information push method according to the present disclosure;
FIG. 3 is an exemplary flow chart of the generation steps of a training sample set according to some embodiments of the present disclosure;
FIG. 4 is a schematic block diagram of some embodiments of an information pushing device according to the present disclosure;
FIG. 5 is a schematic structural diagram of an electronic device suitable for use in implementing some embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 is a schematic diagram of one application scenario of an information push method according to some embodiments of the present disclosure.
As shown in the application scenario of fig. 1, first, the execution subject of the information push method may be the server 101. The server 101 may select the target payment-related information 105 from the sequence of payment-related information 102 based on the first target value 103 and the second target value 104 of the payment-related information in the sequence of payment-related information 102. The payment-related information in the payment-related information sequence 102 has a corresponding first target value and a second target value. The payment-related information may refer to related information generated when the user pays (e.g., full red envelope, withholding volume or consumption credit, etc.). As an example, payment-related information having a first target value 103 of "99" and a second target value 104 of "10" may be selected from the sequence of payment-related information 102 as the target payment-related information 105. Determining whether a conversion rate (e.g., shown as "P") of the target payment-related information 105 of the target user is greater than or equal to a preset threshold (e.g., shown as "H"); the preset threshold may be preset. In response to determining that the conversion rate of target payment-related information 105 of the target user is equal to or greater than a preset threshold, determining whether a rate (for example, shown as "delta" in the figure) of target payment-related information 105, which is obtained based on first target value 103 and second target value 104, satisfies a predetermined condition; the predetermined condition may be that an upper limit value (e.g., "a" is shown in the figure) and a lower limit value (e.g., "b" is shown in the figure) are first set for the rate, and then the rate is determined to be equal to or higher than the lower limit value and equal to or lower than the upper limit value, where the upper limit value is equal to or higher than the upper limit value. In response to determining that the rate of the target payment-related information 105 satisfies a predetermined condition, the target payment-related information 105 is transmitted to the terminal device 106 of the target user. The terminal device 106 may be a terminal device having an information presentation function. For example, the terminal device 106 may be a computer or a mobile phone.
It is understood that the information pushing method may be executed by the server 101, or may be executed by other devices, or may be executed by various software programs. Furthermore, the execution body may be various electronic devices having a display screen, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the execution subject is software, the software can be installed in the electronic device listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
It should be understood that the number of servers and terminal devices in fig. 1 is merely illustrative. There may be any number of servers and terminal devices, as desired for implementation.
With continued reference to fig. 2, a flow 200 of some embodiments of an information push method according to the present disclosure is shown. The information pushing method comprises the following steps:
In some embodiments, an executing entity (e.g., the server 101 shown in fig. 1) of the information push method may select target payment-related information from the payment-related information sequence according to a first target value and a second target value of the payment-related information in the payment-related information sequence, where the payment-related information in the payment-related information sequence has a corresponding conversion rate. The above-mentioned payment-related information may refer to related information generated when the user pays. The first target value and the second target value may be attribute values of the payment-related information. As an example, when the payment-related information is a red pack, the first target value may be a threshold value of the red pack, and the second target value may be a denomination value of the red pack. For example, when the payment-related information is a coupon of full 99 minus 5, the first target value may be "99", and the second target value may be "5". And selecting the target payment related information from the payment related information sequence. The execution body may perform selection based on the determined first target value and second target value, for example, selecting the payment-related information having the first target value of "100" and the second target value of "20" as the target payment-related information. The execution main body may select the target payment-related information from the payment-related information sequence in an order from a large value to a small value of the first target value and a large value of the second target value.
In some optional implementations of some embodiments, the payment-related information sequence is determined by: a set of payment related information is obtained. The execution subject may acquire the payment-related information set through a wired connection manner or a wireless connection manner. Wherein, the executive body can preset a payment upper limit value and a payment lower limit value. The first target value and the second target value are required to satisfy that the first target value is greater than the second target value, the second target value is greater than or equal to a lower payment limit, and the first target value is less than or equal to an upper payment limit. As an example, the above-mentioned payment-related information set may be ' ″ ' 99-10 ', ' 69-15 ', ' 39-5 ', ' 99-5 ', ' 69-10 ', ' 99-20 ', ' 59-5 '. And sequencing the payment related information in the payment related information set according to the sequence from large to small of the first target value of the payment related information in the payment related information set to obtain a first payment related information sequence. For example, the first payment-related information sequence may be ' ″ ' 99-10 ', ' 99-5 ', ' 99-20 ', ' 69-15 ', ' 69-10 ', ' 59-5 ', ' 39-5 '. And sequencing the payment related information with the same first target value in the first payment related information sequence according to the sequence from small to large of the second target value of the payment related information in the first payment related information sequence to obtain the payment related information sequence. For example, the above payment-related information sequence may be '99-5', '99-10', '99-20', 69-10 ', 69-15', '59-5', '39-5'.
In some optional implementations of some embodiments, reference may be further made to fig. 3, which illustrates an exemplary flow 300 of the generation step of the training sample set according to some embodiments of the present disclosure. The generation step of the training sample set comprises the following steps:
In some embodiments, the executing entity (e.g., the server 101 shown in fig. 1) of the training sample set generation step may obtain the historical usage information of the target user through a wired connection manner or a wireless connection manner. Wherein the historical usage information includes: the first target value and the second target value of the payment-related information used by the target user, and the first target value and the second target value of the payment-related information not used by the target user.
In some embodiments, the executing body may generate a conversion rate of the payment related information based on the obtained number corresponding to each piece of payment related information used by the target user and the total number of the payment related information of the target user, so as to obtain a conversion rate set. As an example, if the number of using the payment-related information (e.g., "99-10") may be 2 times and the total number may be 10 times, the conversion rate of the above-described payment-related information may be 20%.
In some embodiments, the executing entity may generate a positive sample set and a negative sample set corresponding to the target user based on the historical usage information, where the positive sample set is obtained by taking a first target value and a second target value of the payment-related information used by the target user as positive samples, and the negative sample set is obtained by taking the first target value and the second target value of the payment-related information not used by the target user as negative samples.
In some embodiments, the execution subject may select a first target number of positive samples from the set of positive samples. The first target number may be a predefined number. The first target number is equal to or less than the number of positive samples in the set of positive samples. As an example, the execution subject may randomly select a first target number of positive samples from the set of positive samples. As another example, the executing entity may select the first target number of positive samples from large to small according to the conversion rate corresponding to the payment related information in the positive sample set.
In some embodiments, the execution subject may select a second target number of negative examples from the set of negative examples. The second target number may be a predefined number. The second target number is equal to or less than the number of negative examples in the negative example set. As an example, the execution subject may randomly select a second target number of negative examples from the set of negative examples. As another example, the executing entity may select a second target number of negative examples from large to small according to the conversion rate corresponding to the payment related information in the negative example set.
In some embodiments, the execution subject may determine whether the first target number and the second target number satisfy a target condition. The target condition may be that the first target number and the second target number are equal.
In some embodiments, the executing entity may, when it is determined that the first target number and the second target number satisfy a target condition, merge the first target number of positive samples and the second target number of negative samples with corresponding conversion rates in the conversion rate set, respectively, to obtain a sample set, and use the sample set as the training sample set. As an example, the samples in the sample set may be each positive sample and corresponding conversion rate, and each negative sample and corresponding conversion rate.
In some embodiments, the executing entity may determine whether a conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold. The preset threshold may be preset.
In some embodiments, the executing entity may determine whether the rate of the target payment-related information satisfies a predetermined condition after determining that the conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold. The predetermined condition may be that the charge rate is determined to be equal to or greater than a charge rate lower limit value and equal to or less than the charge rate upper limit value, based on a charge rate upper limit value and a charge rate lower limit value that are set in advance.
In some embodiments, the executing entity may send the target payment-related information to the terminal device of the target user when determining that the rate of the target payment-related information satisfies a predetermined condition.
In some optional implementations of some embodiments, the method further comprises:
step one, acquiring historical information of the target user; the execution main body can acquire the history information of the target user in a wired connection mode or a wireless connection mode. The history information may be information related to historical value transfer of the target user, information on historical viewed articles, and the like. The above-mentioned historical value transfer related information may be information related to the value transfer of the user (for example, the amount of consumption of the user, a record of purchased articles of the user, or information on the usage of the red envelope coupon by the user, etc.). The history browsing item information may be information on items browsed by the target user or item information preferred by the user (for example, information on items on which the user has added a shopping cart or information on items collected by the user).
Secondly, performing word segmentation processing on the historical information to obtain a word set; here, the word segmentation process may be to perform word segmentation on each piece of history information by using a word segmenter. The word segmentation device is used for segmenting a document into words. As an example, various common chinese word segmenters, or english word segmenters, may be used. The word may be a single word or a word including at least two words.
Thirdly, according to a preset stop word library, performing stop word removal processing on the word set to obtain a word group; here, the Stop word generally means that some Words or phrases are automatically filtered before or after processing natural language data in order to save storage space and improve search efficiency in information retrieval, and these Words or phrases are called Stop Words (Stop Words).
And fourthly, determining the occurrence frequency of each word in the word group.
And fifthly, determining the quantity of the acquired historical information of the target user, including the historical information of the words, for each word in the word group.
Sixthly, determining the weight of each word in the word group based on the times and the number to obtain a weight set; here, the more the above number of times is weighted more, the more the weight is, the less the weight is, the more the above number is, for each word in the word group.
And seventhly, sequencing the words in the word group based on the weight distribution in the weight distribution set to obtain a word sequence. Here, each word in the word group may be sorted according to the weight score from large to small to obtain a word sequence.
In some embodiments, the calculation is simple and fast, and the obtained result is more accurate.
In some optional implementations of some embodiments, determining a weight score for each word in the word group based on the number of times and the number, resulting in a set of weight scores, includes:
the method comprises the following steps of firstly, determining the word quantity of words in the word group.
As an example, it may be determined that the number of words in the word group is n.
And secondly, determining a positive index and a negative index of the words in the word group based on the times and the number.
As an example, there are m indices for each word. For example, each word has 2 indices, one being the number of times and the other being the number. Wherein the above-mentioned number may be determined as a positive indicator because the more the above-mentioned number is, the more the weight is, the larger the weight is for each word in the word group. The more the above number, the less the weight score, the more the number, the less the number may be determined as a negative indicator.
Thirdly, selecting a target word and a target index, and executing the following steps: as an example, the target word may be randomly selected from the word group. The target index may be randomly selected from the indexes of each word.
Step one, responding to the fact that the target index is determined to be a forward index, and determining a numerical value of the forward index of the target word.
As an example, the value of the above-described forward indicator may be determined by the following formula:
wherein the content of the first and second substances,
a value representing the jth index for the ith word. Wherein i is a natural number and has a value range of 1, …, n. j is a natural number and has a value in the range of 1, …, m. The max function is used to find the largest element of the vector or matrix, or the largest of several specified values. The min function is used to return the minimum value in a given set of parameters.
As an example, the value of the above negative indicator may be determined by the following formula:
wherein the content of the first and second substances,
a value representing the jth index for the ith word. Wherein i is a natural number and has a value range of 1, …, n. j is a natural number and has a value in the range of 1, …, m. The max function is used to find the largest element of the vector or matrix, or the largest of several specified values. The min function is used to return the minimum value in a given set of parameters.
And step two, determining the proportion of the numerical value to the sum of the numerical values of the target indexes of each word in the word group based on the numerical value of the target index of the target word. For convenience of calculation, the numerical values of the positive and negative indicators are used
And (4) showing.
As an example, the specific gravity may be determined by the following formula:
wherein the content of the first and second substances,
the value of the jth index representing the ith word is a proportion of the sum of the values of the jth indices of each word in the word group.
A value representing the jth index for the ith word. Wherein i is a natural number and has a value range of 1, …, n. j is a natural number and has a value in the range of 1, …, m.
And step three, determining the entropy value of the target index based on the specific gravity.
As an example, the entropy value of the target index may be determined by the following formula:
wherein the content of the first and second substances,
an entropy value representing a j-th index, wherein,
. ln is a natural logarithm.
Wherein, in the step (A),
. m represents m indices.
And step four, determining the difference coefficient of the target index based on the entropy value.
As an example, the difference coefficient of the target index may be determined by the following formula:
wherein the content of the first and second substances,
the difference coefficient representing the j-th index.
And step five, determining the weight of the target index based on the difference coefficient.
As an example, the weight of the target index may be determined by the following formula:
wherein the content of the first and second substances,
representing the weight of the jth index.
The difference coefficient representing the j-th index. m represents m indices.
And fourthly, determining the weight score of the target word based on the weight of each index of the target word, and obtaining a weight score set based on the weight score of each word in the word group.
As an example, the weight score of the target word may be determined by the following formula:
wherein the content of the first and second substances,
representing the weight division of the ith word.
Representing the weight of the jth index.
The ith word under the jth index accounts for the weight of the index. j is a natural number and has a value in the range of 1, …, m.
The optional implementation mode determines the weight of the index according to the variation degree of the index value of each index, and the method is an objective weighting method and avoids deviation caused by human factors.
In some optional implementations of some embodiments, the method further comprises:
firstly, selecting a target number of words from the word sequence according to the sequence of weight scores from large to small; the target number may be a preset number, or may be a product of the number of words in the word group and the target ratio. For example, the number of words in the above-described word group is 10, the target proportion may be 30%, and then the target number may be 3.
And secondly, generating a word vector of each word in the target number of words to obtain a word vector set. For example, Word Embedding (Word Embedding) may be performed on each Word in the target number of words to obtain a Word vector of each Word. The word embedding generally refers to a technique of converting a word expressed in a natural language into a vector or matrix form that can be understood by a computer.
And thirdly, generating an information vector based on the word vector set, and determining the information vector as the user characteristics of the target user. As an example, the execution body may generate the above information vector by way of summation. As an example, the execution subject may add the word vectors in the word vector set using an addition method to generate the information vector. As an example, the execution body may generate the information vector by adding word vectors in the word vector set by an averaging method and averaging the added word vectors.
In some optional implementation manners of some embodiments, based on the user characteristic and a cross characteristic, a label is added to a machine learning model trained by using the payment-related information characteristic, so as to obtain a label set, where the cross characteristic is generated based on the user characteristic and the payment-related information characteristic, the machine learning model is trained by using a training sample set, a training sample in the training sample set includes a sample payment-related information characteristic and a sample conversion rate corresponding to the sample payment-related information characteristic, and the sample payment-related information characteristic includes a sample first target value, a sample second target value, and a sample rate. The conversion rate may be a usage ratio of the target user to the sample payment related information corresponding to the sample payment related information feature. As an example, the user characteristics described above include at least one of: average user single attribute, average total attribute, price related information and user payment related information utilization rate. The cross-over feature may be a ratio between the user feature and the payment-related information feature. As an example, each user may have a user characteristic and a payment related information characteristic belonging to the user, and a label is added to the machine learning model obtained by training using the payment related information characteristic by using the user characteristic, so that it can be ensured that each user has a corresponding machine learning model, and each machine learning model has a corresponding label. The label may be a number. For example, it may be "0001".
As an example, the machine learning model may be derived by performing the following training steps based on a set of training samples: and respectively inputting the sample payment related information characteristics of at least one training sample in the training sample set into the initial machine learning model to obtain the conversion rate corresponding to each sample payment related information characteristic in the at least one training sample. And comparing the conversion rate corresponding to the payment related information characteristic of each sample in the at least one training sample with the corresponding sample conversion rate. And determining the prediction accuracy of the initial machine learning model according to the comparison result. And determining whether the prediction accuracy is greater than a preset accuracy threshold. And in response to determining that the accuracy is greater than the preset accuracy threshold, taking the initial machine learning model as a trained machine learning model. And adjusting parameters of the initial machine learning model in response to the determination that the accuracy is not greater than the preset accuracy threshold, forming a training sample set by using unused training samples, using the adjusted initial machine learning model as the initial machine learning model, and executing the training step again.
It will be appreciated that after the above training, the machine learning model may be used to characterize the correspondence between the payment-related information features and the conversion rate. The machine learning model mentioned above may be a Logistic Regression (LR) model.
In some alternative implementations of some embodiments, the above conversion is obtained by: and determining the target label corresponding to the target user from the label set based on the user characteristics of the target user. For example, the target annotation for the target user may be "0001". And determining a machine learning model for the target user based on the target label. Here, based on the target label "0001", the corresponding machine learning model is found. And inputting the first target value, the second target value and the charge rate into the machine learning model to obtain the conversion rate. Wherein determining the conversion rate by a model can improve speed and accuracy.
In some optional implementations of some embodiments, the method further comprises: and receiving a user use request, wherein the user use request is a user use request for the payment related information. As an example, the above-described use request may be a request transmitted to the execution subject by the terminal device of the user. When the user clicks the payment-related information displayed on the terminal device, the terminal device sends a request to the execution main body. The number of requests for user usage requests received by each of the at least one zone is determined. The at least one area may be based on a city partition. Or may be based on urban divisions in cities. For example, each city may be a region. Each urban area may also be an area. Comparing the request quantity of each region in the at least one region with the target quantity respectively to obtain a comparison result and generate a comparison result set; the target number may be predetermined or may be an average number of received requests per area. And adjusting the size of the memory corresponding to each area in the target server based on the comparison result in the comparison result set. As an example, when the comparison result is that the number of requests is greater than or equal to the target number, the memory corresponding to the area is increased. And when the comparison result is that the request number is smaller than the target number, reducing the memory corresponding to the area.
In some optional implementations of some embodiments, target control information is obtained, wherein the target control information includes at least one of: the conversion rate threshold value after regulation, the rate upper limit value, the rate lower limit value, the target upper limit value after regulation and the target lower limit value after regulation and control. The target regulation and control information can be acquired in a wired or wireless manner. For example, it may be that "sales today need to rise as much as yesterday", then the post-regulation conversion threshold also needs to be larger than the conversion threshold before regulation. Determining whether a first target value of the target payment related information is greater than or equal to the regulated target lower limit value or not in response to that the payment related information conversion rate of the target payment related information is greater than or equal to the regulated conversion rate threshold value; in response to determining that the first target value of the target payment related information is greater than or equal to the regulated target lower limit value, determining whether the first target value is less than the second target value; in response to determining that the first target value is less than the second target value, determining whether the second target value is less than or equal to the regulated target upper limit; determining whether the charge rate is greater than or equal to the charge rate lower limit value in response to determining that the second target value is less than or equal to the regulated target upper limit value; determining whether the rate is less than or equal to the upper rate limit in response to determining that the rate is greater than or equal to the lower rate limit; and generating target regulation and control payment related information in response to determining that the rate is less than or equal to the rate upper limit value. As an example, the executing entity may use the target payment related information as target regulated payment related information and send the target regulated payment related information to the terminal device of the target user. The target regulation and control payment related information generated according to the control information can attract users, and meanwhile, the operation target corresponding to the target regulation and control information can be achieved more easily.
In the information push method disclosed in some embodiments of the present disclosure, first, target payment-related information is selected from the payment-related information sequence according to a first target value and a second target value of the payment-related information in the payment-related information sequence. Then, whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value is determined. This ensures that the conversion rate of the target payment-related information is maintained within a certain range. Then, in response to determining that the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold, determining whether the rate of the target payment related information meets a preset condition; by determining the value range of the rate, the cost can be ensured not to be too high. And finally, in response to determining that the rate of the target payment related information meets a preset condition, sending the target payment related information to the terminal equipment of the target user. According to the embodiment, the payment related information with the larger first target value is sent to the user, so that the click rate of the user on the payment related information is improved, and the network flow is increased.
With further reference to fig. 4, as an implementation of the above method for the above figures, the present disclosure provides some embodiments of an information pushing apparatus, which correspond to those of the method embodiments described above in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 4, the information pushing apparatus 400 of some embodiments includes: a selection unit 401, a first determination unit 402, a second determination unit 403 and a transmission unit. The selection unit 401 is configured to select target payment related information from the payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence, where the payment related information in the payment related information sequence has a corresponding conversion rate; a first determining unit 402 configured to determine whether a conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold; a second determination unit 403 configured to determine whether a rate of the target payment-related information satisfies a predetermined condition in response to determining that a conversion rate of the target payment-related information of a target user is equal to or greater than a preset threshold, wherein the rate is obtained based on the first target value and the second target value; and a sending unit 404 configured to send the target payment-related information to the terminal device of the target user in response to determining that the rate of the target payment-related information satisfies a predetermined condition.
In some optional implementations of some embodiments, the payment-related information sequence is determined by: acquiring a payment related information set, wherein the payment related information in the payment related information set has a corresponding first target value and a second target value; sequencing the payment related information in the payment related information set according to the sequence from large to small of a first target value of the payment related information in the payment related information set to obtain a first payment related information sequence; and sequencing the payment related information with the same first target value in the first payment related information sequence according to the sequence from small to large of the second target value of the payment related information in the first payment related information sequence to obtain the payment related information sequence.
In some optional implementations of some embodiments, the information pushing device 400 is further configured to: acquiring a historical information set of the target user; performing word segmentation processing on the historical information set to obtain a word set; according to a preset stop word library, performing stop word removal processing on the word set to obtain a word group; determining the occurrence frequency of each word in the word group; for each word in the word group, determining the number of the acquired historical information of the target user, including the historical information of the word; determining the weight of each word in the word group based on the times and the number to obtain a weight distribution set; and sequencing the words in the word group based on the weight distribution in the weight distribution set to obtain a word sequence.
In some optional implementations of some embodiments, the information pushing device 400 is further configured to: selecting a target number of words from the word sequence according to the sequence of weight scores from large to small; generating a word vector of each word in the target number of words to obtain a word vector set; and generating an information vector based on the word vector set, and determining the information vector as the user characteristics of the target user.
In some optional implementations of some embodiments, the information pushing device 400 is further configured to: and adding a label to a machine learning model obtained by training the payment-related information features based on the user features and cross features to obtain a label set, wherein the cross features are generated based on the user features and the payment-related information features, the machine learning model is obtained by training through a training sample set, training samples in the training sample set comprise sample payment-related information features and sample conversion rates corresponding to the sample payment-related information features, and the sample payment-related information features comprise a sample first target value, a sample second target value and a sample rate.
In some alternative implementations of some embodiments, the above conversion is obtained by: determining a target label corresponding to the target user from the label set based on the sample characteristics of the target user; determining a machine learning model for the target user based on the target label; and inputting the first target value, the second target value and the charge rate into the machine learning model to obtain the conversion rate.
In some optional implementations of some embodiments, the training sample set is generated according to the following steps: obtaining historical use information of the target user, wherein the historical use information comprises: a first target value and a second target value of the payment-related information used by the target user, and a first target value and a second target value of the payment-related information not used by the target user; generating a conversion rate of the payment related information based on the obtained number corresponding to each piece of payment related information used by the target user and the total number of the payment related information of the target user, and obtaining a conversion rate set; generating a positive sample set and a negative sample set corresponding to the target user based on the historical use information, wherein the positive sample set is obtained by taking a first target value and a second target value of the payment related information used by the target user as positive samples, and the negative sample set is obtained by taking the first target value and the second target value of the payment related information not used by the target user as negative samples; selecting a first target number of positive samples from the positive sample set; selecting a second target number of negative samples from the negative sample set; determining whether the first target number and the second target number satisfy a target condition; and in response to determining that the first target number and the second target number meet a target condition, merging the first target number of positive samples and the second target number of negative samples with corresponding conversion rates in the conversion rate set to obtain a sample set, and taking the sample set as the training sample set.
In some optional implementations of some embodiments, the information pushing device 400 is further configured to: receiving a user use request, wherein the user use request is a use request of a user for payment related information; determining the number of requests of user use requests received by each area in at least one area; comparing the request quantity of each region in the at least one region with the target quantity respectively to obtain a comparison result and generate a comparison result set; and adjusting the size of the memory corresponding to each area in the target server based on the comparison result in the comparison result set.
It will be understood that the elements described in the apparatus 400 correspond to various steps in the method described with reference to fig. 2. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 400 and the units included therein, and will not be described herein again.
Referring now to fig. 5, a schematic diagram of an electronic device (e.g., the server of fig. 1) 500 suitable for use in implementing some embodiments of the present disclosure is shown. The terminal device in some embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. The terminal device shown in fig. 5 is only an example, and should not bring any limitation to the functions and the use range of the embodiments of the present disclosure.
As shown in fig. 5, electronic device 500 may include a processing means (e.g., central processing unit, graphics processor, etc.) 501 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 502 or a program loaded from a storage means 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data necessary for the operation of the electronic apparatus 500 are also stored. The processing device 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
Generally, the following devices may be connected to the I/O interface 505: input devices 506 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 507 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; a storage device 508 including, for example, a memory card; and a communication device 509. The communication means 509 may allow the electronic device 500 to communicate with other devices wirelessly or by wire to exchange data.
While fig. 5 illustrates an electronic device 500 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 5 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network via the communication means 509, or installed from the storage means 508, or installed from the ROM 502. The computer program, when executed by the processing device 501, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the computer readable medium described above in some embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In some embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: selecting target payment related information from the payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence, wherein the payment related information in the payment related information sequence has a corresponding conversion rate; determining whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value; determining whether the rate of the target payment-related information meets a predetermined condition in response to determining that the conversion rate of the target payment-related information of the target user is greater than or equal to a preset threshold; and sending the target payment related information to the terminal equipment of the target user in response to the fact that the rate of the target payment related information meets the preset condition.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, and may be described as: a processor includes a selection unit, a first determination unit, a second determination unit, and a transmission unit. The names of these units do not in some cases constitute a limitation on the units themselves, and for example, the selection unit may also be described as a "unit that selects target payment-related information from the above-described payment-related information sequence on the basis of a first target value and a second target value of the payment-related information in the payment-related information sequence".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (6)
1. An information push method, comprising:
selecting target payment related information from a payment related information sequence according to a first target value and a second target value of the payment related information in the payment related information sequence, wherein the payment related information in the payment related information sequence has a corresponding conversion rate, the first target value and the second target value are attribute values of the payment related information, when the payment related information is a red packet, the first target value is a threshold value of the red packet, the second target value is a denomination value of the red packet, and the payment related information is related information generated when a user pays;
determining whether the conversion rate of the target payment related information of the target user is greater than or equal to a preset threshold value;
determining whether a rate of the target payment-related information of a target user satisfies a predetermined condition in response to determining that a conversion rate of the target payment-related information is equal to or greater than a preset threshold, wherein the rate is obtained based on the first target value and the second target value, the predetermined condition is that the rate is equal to or greater than a rate lower limit, and the rate is equal to or less than a rate upper limit;
in response to determining that the rate of the target payment-related information meets a predetermined condition, sending the target payment-related information to a terminal device of the target user;
acquiring historical information of the target user, wherein the historical information comprises historical value transfer related information and historical browsing item information of the target user;
performing word segmentation processing on the historical information to obtain a word set, wherein the words comprise at least one of the following items: a single word comprising at least two words;
according to a preset stop word library, performing stop word removal processing on the word set to obtain a word group;
determining a number of times each term in the set of terms occurs in the set of terms;
for each word in the word group, determining the quantity of the acquired historical information of the target user, wherein the historical information comprises the historical information of the word;
determining a weight score of each word in the word group based on the number of times and the number, obtaining a weight score set, wherein the more times the number of times each word in the word group appears, the more times the number is, the higher the weight score is, the smaller the number is;
sequencing the words in the word group based on the weight distribution in the weight distribution set to obtain a word sequence;
wherein the determining the weight score of each word in the word group based on the number of times and the number to obtain a weight score set comprises: determining the number of words of the words in the word group, determining a positive index and a negative index of the words in the word group based on the number of times and the number, wherein the more the number of times each word in the word group appears, the higher the weight score is, the more the number is, the more the weight score is, the more the number is, the smaller the weight score is, the number is determined as the negative index, selecting a target word and a target index, wherein the target word is randomly selected from the word group, and executing the following steps: determining a value of a forward indicator of the target word in response to determining that the target indicator is a forward indicator, wherein the value of the forward indicator is determined by the following equation:
wherein, XijA value representing the jth index of the ith word, i being fromThe numerical value range is 1, …, n, j is a natural number, the numerical value range is 1, …, m, max function is used for solving the maximum element of a vector or a matrix or the maximum value of a plurality of specified values, the min function is used for returning the minimum value in a given group of parameters, and the numerical value of the negative index is determined by the following formula:
wherein, based on the value of the target indicator of the target word, a proportion of the value to a sum of the values of the target indicators of each word in the word group is determined, the proportion being determined by the following formula:
wherein, PijA proportion of a value of a jth index representing an ith word to a sum of values of jth indices of each word in the word group, an entropy value of the target index being determined based on the proportion, the entropy value of the target index being determined by the following formula:
wherein e isjEntropy representing the j index, ejNot less than 0, ln is a natural logarithm, k is 1/ln m, k > 0, m represents m indexes, based on the entropy value, the difference coefficient of the target index is determined by the following formula: gj＝1-ejWherein g isjA difference coefficient representing the j-th index, and determining the weight of the target index based on the difference coefficient, wherein the weight of the target index is determined by the following formula:
wherein, WjA weight representing the jth index, determining a weight score for the target term based on the weight of each index of the target term, and deriving a weight based on the weight scores for each term in the group of termsThe set of scores, the weight score of the target word, is determined by the following formula:
wherein S isiA weight score representing the ith word;
selecting a target number of words from the word sequence according to the sequence of weight scores from large to small;
generating a word vector of each word in the target number of words to obtain a word vector set;
generating an information vector based on the word vector set, and determining the information vector as the user characteristics of the target user;
adding labels to a machine learning model obtained by training with the payment related information features based on the user features and cross features to obtain a label set, wherein the cross features are generated based on the user features and the payment related information features, the machine learning model is obtained by training through a training sample set, training samples in the training sample set comprise sample payment related information features and sample conversion rates corresponding to the sample payment related information features, and the sample payment related information features comprise a sample first target value, a sample second target value and a sample rate;
the conversion is obtained by the following steps:
determining a target label corresponding to the target user from the label set based on the user characteristics of the target user;
determining a machine learning model for the target user based on the target annotation;
and inputting the first target value, the second target value and the rate into the machine learning model to obtain the conversion rate.
2. The method of claim 1, wherein the sequence of payment-related information is determined by:
acquiring a payment related information set;
sequencing the payment related information in the payment related information set according to the sequence from large to small of the first target value of the payment related information in the payment related information set to obtain a first payment related information sequence;
and sequencing the payment related information with the same first target value in the first payment related information sequence according to the sequence from small to large of the second target value of the payment related information in the first payment related information sequence to obtain the payment related information sequence.
3. The method of claim 1, wherein the set of training samples is generated according to the following steps:
obtaining historical use information of the target user, wherein the historical use information comprises: a first target value and a second target value of the payment-related information used by the target user, the first target value and the second target value of the payment-related information not used by the target user;
generating a conversion rate of the payment related information based on the obtained number corresponding to each piece of payment related information used by the target user and the total number of the payment related information of the target user, so as to obtain a conversion rate set;
generating a positive sample set and a negative sample set corresponding to the target user based on the historical use information, wherein a first target value and a second target value of the payment related information used by the target user are used as positive samples to obtain the positive sample set, and a first target value and a second target value of the payment related information not used by the target user are used as negative samples to obtain the negative sample set;
selecting a first target number of positive samples from the positive sample set;
selecting a second target number of negative samples from the negative sample set;
determining whether the first target number and the second target number satisfy a target condition;
in response to determining that the first target number and the second target number meet a target condition, merging the first target number of positive samples and the second target number of negative samples with corresponding conversion rates in the conversion rate set respectively to obtain a sample set, and taking the sample set as the training sample set.
4. The method according to one of claims 1-3, wherein the method further comprises:
receiving a user use request, wherein the user use request is a user use request for payment related information;
determining the number of requests of user use requests received by each area in at least one area;
comparing the request quantity of each region in the at least one region with the target quantity respectively to obtain a comparison result and generate a comparison result set;
and adjusting the size of the memory corresponding to each area in the target server based on the comparison result in the comparison result set.
5. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-3.
6. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-3.
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